# Model Card for Model ID

Intent classification is the act of classifying customer's in to different pre defined categories. 
Sometimes intent classification is referred to as topic classification.
By fine tuning a T5 model with prompts containing sythetic data that resembles customer's requests this 
model is able to classify intents in a dynamic way by adding all of the categories to the prompt


## Model Details
Fine tuned Flan-T5-Base

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Serj Smorodinsky
- **Model type:** Flan-T5-Base
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Flan-T5-Base

### Model Sources [optional]

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- **Repository:** https://github.com/SerjSmor/intent_classification


## How to Get Started with the Model



[More Information Needed]

## Training Details

### Training Data

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https://github.com/SerjSmor/intent_classification
HF dataset will be added in the future.

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
https://github.com/SerjSmor/intent_classification/blob/main/t5_generator_trainer.py

Using HF trainer

    training_args = TrainingArguments(
        output_dir='./results',
        num_train_epochs=epochs,
        per_device_train_batch_size=batch_size,
        per_device_eval_batch_size=batch_size,
        warmup_steps=500,
        weight_decay=0.01,
        logging_dir='./logs',
        logging_steps=10,
        evaluation_strategy="epoch"
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=val_dataset,
        tokenizer=tokenizer,
        # compute_metrics=compute_metrics
    )



## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->
I've used Atis dataset for evaluation. 
F1 AVG on the train set is 0.69


#### Summary



#### Hardware

Nvidia RTX3060 12Gb